Automated Author ProfileMiliczek, Pascal
Universität Stuttgart0009-0006-2831-0870
Miliczek, Pascal
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 8.8 (sum of 19 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Prepared datasets and models for modeling orientational variation in heat plume prediction in groundwater.<br>Models were trained with <a href="https://github.com/pLm-k/1HP_NN_equivariance/tree/release_24">1HP NN equivariance</a>. <br><br><strong>File name explanation:</strong><br><dl> <dt>4d</dt> <dd>The (used) dataset encompasses only cardinal flow directions.</dd><dt>rd</dt> <dd>The (used) dataset encompasses random flow directions in the 2D plane.</dd> <dt>1000dp</dt> <dd>The (used) dataset consists of 1000 data points.</dd><dt>100dp</dt> <dd>The (used) dataset consists of 100 data points.</dd><dt>pksi</dt> <dd>The input data fields: liquid pressure, permeability, position of the heat pump, and normalized distance to the heat pump.</dd><br><dt>BaselineCNN</dt> <dd>The model is trained without any modification to architecture/training procedure.</dd><dt>DataAugmentation</dt> <dd>The model is trained using data augmentation where rotated variations of the original data were added to the training data.</dd><dt>OrientedBoxes</dt> <dd>For this model, during both training and inference, the input data is aligned to a chosen orientation.</dd><dt>ECNN</dt> <dd>The model uses the Equivariant Convolutional Neural Network (ECNN) architecture.</dd></dl>
Authors
- Miliczek, Pascal
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Authors
- Miliczek, Pascal
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Authors
- Miliczek, Pascal
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Authors
- Miliczek, Pascal
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Authors
- Miliczek, Pascal
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Authors
- Miliczek, Pascal
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Authors
- Miliczek, Pascal
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Authors
- Miliczek, Pascal
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Authors
- Miliczek, Pascal